Autogenerating video from text

ABSTRACT

A method of converting user-selected printed text to a synthesized image sequence is provided. The method includes capturing a first image of printed text and generating a model information associated with the text.

CROSS-REFERENCE TO RELATED PATENT APPLICATIONS

This application is a continuation of application Ser. No. 14/880,765,filed Oct. 12, 2015, now U.S. Pat. No. 9,552,515, which is acontinuation of application Ser. No. 14/686,303, filed Apr. 14, 2015,now U.S. Pat. No. 9,189,698, which is a continuation of application Ser.No. 14/262,264, filed Apr. 25, 2014, now U.S. Pat. No. 9,036,950, whichis a divisional of application Ser. No. 13/355,194, filed Jan. 20, 2012,now U.S. Pat. No. 8,731,339, all of which are incorporated herein byreference in their entireties.

BACKGROUND

The present application relates generally to the field of generatingsynthesized image sequences. The present application relates morespecifically to the field of generating synthesized image sequencesbased on a selected textual passage.

School textbooks are notorious for their dry presentation of material.Paintings or photographs are often included in the textbook to maintainthe student's interest and to provide context to the subject matterbeing conveyed. However, due to limited space, only a limited number ofimages may be included in the textbook. Further, students with dyslexia,attention deficit disorder, or other learning disabilities may havedifficulty reading long passages of text. Thus, there is a need forimproved systems and methods of conveying the subject matter underlyingthe text to a reader.

SUMMARY OF THE INVENTION

One embodiment relates to a method of converting user-selected printedtext to a synthesized image sequence. The method includes capturing afirst image of printed text and generating a model informationassociated with the text.

Another embodiment relates to a system for converting user-selectedprinted text to a synthesized image sequence. The system includesprocessing electronics configured to receive an image of text and, inresponse to receiving the image, to generate a model informationassociated with the text.

Another embodiment relates to a computerized method of sharing asynthesized image sequence generated from user-selected text. The methodincludes generating a model information associated with a textualpassage and exporting a file, the file configured to enable another userto generate a synthesized image sequence.

Another embodiment relates to a system for converting a textual passageto a synthesized image sequence. The system includes processingelectronics configured to determine a first textual passage being readby a user, to predict a second textual passage that will be read by theuser, and to generate a synthesized image sequence associated with thetextual passage.

Another embodiment relates to a method of converting a textual passageto a synthesized image sequence. The method includes determining a firsttextual passage currently being read by a user; predicting a secondtextual passage that will be read by the user; and generating asynthesized image sequence associated with the second textual passage.

Another embodiment relates to a system for converting a textual passageto a synthesized image sequence. The system includes processingelectronics configured to generate a model information associated with atextual passage in response to the textual passage being written.

Another embodiment relates to a computerized method of converting atextual passage to a synthesized image sequence. The method includesgenerating a model information associated with a textual passage inresponse to the textual passage being written.

The foregoing is a summary and thus by necessity containssimplifications, generalizations and omissions of detail. Consequently,those skilled in the art will appreciate that the summary isillustrative only and is not intended to be in any way limiting. Otheraspects, inventive features, and advantages of the devices and/orprocesses described herein, as defined solely by the claims, will becomeapparent in the detailed description set forth herein and taken inconjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of text and an image capture device, shownaccording to an exemplary embodiment.

FIG. 2 is a schematic diagram of a server, client, image capture device,and a display connected over a network and configured for using thesystems and methods of this disclosure, shown according to an exemplaryembodiment.

FIG. 3 is a detailed block diagram of processing electronics, shownaccording to an exemplary embodiment.

FIG. 4 is a flowchart of a process for converting a user-selectedprinted text to a synthesized image sequence, shown according to anexemplary embodiment.

FIG. 5 is a flowchart of a process for converting a user-selectedprinted text to a synthesized image sequence, shown according to anotherembodiment.

FIG. 6 is a flowchart of a process for converting a user-selectedprinted text to a synthesized image sequence, shown according to anotherembodiment.

FIG. 7 is a flowchart of a process for converting a user-selectedprinted text to a synthesized image sequence, shown according to anotherembodiment.

FIG. 8 is a flowchart of a process for converting a user-selectedprinted text to a synthesized image sequence, shown according to anotherembodiment.

FIG. 9 is a flowchart of a process for converting a user-selectedprinted text to a synthesized image sequence, shown according to anotherembodiment.

FIG. 10 is a flowchart of a process for sharing a synthesized imagesequence generated from a user-selected text, shown according to anexemplary embodiment.

FIG. 11 is a flowchart of a process for sharing a synthesized imagesequence generated from a user-selected text, shown according to anotherembodiment.

FIG. 12 is a flowchart of a process for converting a textual passage toa synthesized image sequence, shown according to an exemplaryembodiment.

FIG. 13 is a flowchart of a process for converting a textual passage toa synthesized image sequence, shown according to another embodiment.

FIG. 14 is a flowchart of a process for converting a textual passage toa synthesized image sequence, shown according to another embodiment.

FIG. 15 is a flowchart of a process for converting a textual passage toa synthesized image sequences, shown according to an exemplaryembodiment.

FIG. 16 is a flowchart of a process for converting a textual passage toa synthesized image sequences, shown according to another embodiment.

FIG. 17 is a flowchart of a process for converting a textual passage toa synthesized image sequences, shown according to another embodiment.

FIG. 18 is a flowchart of a process for converting a user-selectedprinted text to a synthesized image sequence, shown according to anotherembodiment.

DETAILED DESCRIPTION

Referring generally to the Figures, systems and methods for thegeneration of synthesized image sequences (e.g., video) based on printedtext are shown and described. A person may be reading a block of textfrom a textual passage (e.g., from a book, magazine, journal, electronicbook (e-book), computer, cell phone, a paper with handwritten text,portion of a painting, newspaper, or any other source of text). An imageof the block of text is captured and the block of text may be analyzedto determine the actual text and the context of the text. A modelinformation may then be generated that is representative of the text.The model information may be of any format (e.g., wireframe, solid,shell, boundary, two-dimensional, three-dimensional, etc.) in anylanguage (e.g., markup language, extensible markup language (XML)virtual reality markup language (VRML), X3D, 3DXML, etc.). The modelinformation may be output as a file or streamed, for example, to arendering engine. A synthesized image sequence may then be generatedbased on the model information. The synthesized image sequence may be ofany format (e.g., a series of pictures, a single video, a cartoon, a 2Dvideo, a 3D video, etc.) and used in many types of media. While thepresent disclosure uses the term “video” or “video clip” to oftendescribe the synthesized image sequence, it should be understood thatany type of synthesized image sequence may be generated using thesystems and methods of the present disclosure.

The synthesized image sequence may be generated using a preference filein addition to the block of text. The preference file indicates userpreferences to be applied when the video clip is generated. Thepreference file may include types of people or characters to include inthe video clip, setting information, and other information that affectsthe display of the synthesized image sequence. According to oneembodiment, the model information may be generated based on thepreference file. According to another embodiment, the synthesized imagesequence may be generated based on the model information and thepreference file.

The systems and methods of the present disclosure may be used to predicttext that a person is about to read, and to generate a synthesized imagesequence based on the predicted text. For example, a reading speed ofthe person may be measured or provided and synthesized image sequencesfor “upcoming” text may be generated in anticipation of the personreading the text. Further, the systems and methods of the presentdisclosure may be used in real-time (e.g., for real-time typing orhandwriting, synthesized image sequences may be generated as the text isbeing generated by the person).

According to one contemplated scenario, a student is assigned a readingassignment. To make the assignment more interesting, the student may usehis or her mobile phone to take a picture of a page of the textbook. Thesystems and methods described herein may then generate a synthesizedimage sequence of the action occurring in the text. Thus, rather thansimply reading names and dates, the student may see soldiers runningacross a battlefield. The systems and methods may further gatherauxiliary information (e.g., the color of the soldiers' uniforms, thetopographical layout of the battlefield, what the generals looked like,time of year, weather conditions, etc.), which may be incorporated intothe synthesized image sequences. Presenting the information in a visual,rather than textual, fashion may help to put the information in contextand to create cross-references in the student's brain which may help thestudent to recall the information at a later date. The student may thenshare one or more files with classmates, enabling them to generate thefinished sequences. For example, the student may share his or herfinished segments, a model information, a preference file, or otherfiles necessary to generate the image sequence.

For purposes of this disclosure, the term “coupled” means the joining oftwo members directly or indirectly to one another. Such joining may bestationary in nature or moveable in nature and such joining may allowfor the flow of fluids, electricity, electrical signals, or other typesof signals or communication between the two members. Such joining may beachieved with the two members or the two members and any additionalintermediate members being integrally formed as a single unitary bodywith one another or with the two members or the two members and anyadditional intermediate members being attached to one another. Suchjoining may be permanent in nature or alternatively may be removable orreleasable in nature.

Referring to FIG. 1, a block of text 102 is shown. The block of text 102may be part of a textual passage provided from any source (e.g., a book,magazine, electronic book, etc.). An image capture device 104 maycapture an image of the block of text 102. The image capture device 104may be a camera, scanner, or other electronic device configured tocapture an image including text. The camera may be a standalone camera,coupled to a mobile phone, coupled to a laptop, desktop, or othercomputing device, coupled to any personal electronic device, orotherwise. The systems and methods of the present disclosure are notlimited based on the type of image capture device.

The block of text 102 may be any type of text from any type of source.As one example, the block of text 102 may be typeset text, for example,from a portion of a book, from a portion of a magazine, from a portionof a newspaper, etc., may be text on an electronic computer, may behandwritten text, may be text from a painting or drawing, may be textfrom an inscription, or otherwise. The systems and methods of thepresent disclosure are not limited based on the type of source of thetext and the type of the text being captured by the image capturedevice. The block of text 102 may be chosen by a user. As one example,if the text is on an electronic display, the user may select the textvia the electronic device and manually take a picture of the display. Asanother example, the user may choose a portion of the text and scan thepage. As another example, the block of text 102 may simply be aselection of a sentence, paragraph, page, chapter, or another logicalgrouping of the text that is automatically determined by the imagecapture device 104. As another example, before capturing the image ofthe printed text, the user may indicate (e.g., underline, highlight,bracket, etc.) the portion of the printed text for which the userdesires a synthesized image sequence.

Referring now to FIG. 2, a block diagram of a system 200 for executingthe systems and methods of the present disclosure is shown, according toan exemplary embodiment. System 200 includes an image capture device 104for capturing images of text. The image capture device 104 is furtherconnected to a network 210 for sending the images of text. The imagecapture device 104 is shown as a mobile phone; but may be any other typeof electronic device for capturing images as described in FIG. 1. Forexample, the image capture device may be a camera configured to connectvia a wired or wireless connection with a laptop or personal computer,which then connects to the network 210. As another example, the imagecapture device may be a scanner that has a wired or wireless connectionto the network 210.

The image capture device 104 as shown in FIG. 2 includes a camera 202configured to capture the image of text. The image capture device 104(e.g., mobile phone, digital camera, personal digital assistant,scanner, etc.) may further include a display 204 which a user may use toview the captured image of text. The image capture device 104 furtherincludes user input devices 206 (e.g., buttons) that allow a user tocontrol the image capture device 104 (e.g., to capture the images oftext, to select portions of the image of text, to save the images oftext, to send the images of text to network 210, etc.). The user inputdevices 206 may include a keypad, keyboard, trackball, mouse, softbutton, or other interfaces. The images of text may be stored in amemory of the image capture device 104 or output to another device forinterpreting the images of text. While the image capture device 104 isdepicted as communicating with a client 230, 240 over the network 210,according to various embodiments, the image capture device 104 mayincorporate or be coupled to the client 230, 240 (e.g., mobile phone,personal digital assistant, etc.) or may be wired or coupled to theclient 230, 240 (e.g., connecting a digital camera or scanner to apersonal computer) rather than communicating with the client 230, 240over the network 210.

The network 210 may be a network such as a local area network (LAN), awide area network (WAN), the Internet, or a combination thereof. Thenetwork 210 connects the image capture device 104, a server 220, one ormore clients 230, 240, and a display 250. According to various exemplaryembodiments, the system 200 may include any number of image capturedevices, servers, clients, and displays. For example, the embodiment ofFIG. 2 illustrates two clients and a display; the system 200 may includeonly clients that request synthesized image sequences, only displaysthat are configured to receive a synthesized image sequence, or anycombination thereof.

The system 200 is shown to include a server 220. The server 220 includesprocessing electronics 224. According to one embodiment, the server 220and more particularly the processing electronics 224 are configured toreceive an image of text from an image capture device 104 via thenetwork 210. The server 220 and processing electronics 224 analyze theimage of text and generate a model information associated with the text.The server 220 and processing electronics 224 may then generate (e.g.,render, etc.) a synthesized image sequence (e.g., video clip) based onthe model information. The server 220 then provides the synthesizedimage sequence to the display 250 or the clients 230, 240 for output toa user. The processing electronics 224 are configured to generate themodel information and synthesized image sequence as shown in greaterdetail in FIG. 3. The server 220 may be configured to automaticallygenerate a model information or synthesized image sequence in responseto receipt of an image of text, or may be configured to wait for furtheruser input or a given amount of time before generation of the modelinformation or synthesized image sequence. According to anotherembodiment, the server 220 is configured to receive a machine readableformat version of the image of the text. For example, the image capturedevice 104 or the client 230, 240 may translate the image of the text toa machine readable format, for example, via optical characterrecognition, and then send the machine readable format version to theserver 220.

The server 220 may be configured to receive one or more preference filesin addition to the images of text. The preference files are files thatinclude user display preferences regarding the synthesized imagesequences. For example, the preference files may indicate a preferencein the type of character or person displayed in a synthesized imagesequence, a setting shown in the background of the synthesized imagesequence, or otherwise. The processing electronics 224 are configured toreceive the preference files and to use the preference files forgeneration of the synthesized image sequence.

The server 220 may be configured to receive a second image from theimage capture device 104 or another source. The second image may be ofmore text, of auxiliary information relating to the text, of acharacter, or of a setting. For example, the server 220 may receive anadditional passage of the text, an image of an ISBN number of a book, atitle of an article or book, a bar code, or another identifier. Theserver 220 and processing electronics 224 may be configured to interpretthe image of the auxiliary information.

System 200 further includes multiple clients 230, 240 as describedabove. The clients 230, 240 are configured to request or receivesynthesized image sequences from the server 220. In one embodiment, theclients 230, 240 request the server 220 to create the model informationor synthesized image sequences. In another embodiment, the server 220 isconfigured to send synthesized image sequences to the clients 230, 240upon receipt of a request or images from the image capture device 104 oranother device. The clients 230, 240 may be clients that want to orderthe model information or synthesized image sequences as part of asubscription, according to one embodiment. It should be understood thatthe clients 230, 240 may interact with the system 200 in various ways.For example, the clients 230, 240 may be configured to be any of thedevices in the subsequent figures for interacting with the varioussystems of the present disclosure.

While the clients 230, 240 and the display 250 are shown as separatedevices and described as such in the embodiment of FIG. 2, the clients230, 240 in the system 200 may be the same device as the image capturedevice 104, or the display 250 may be the display 204 of the imagecapture device 104. For example, the image capture device 104 mayprovide an image of text to the server 220 and then request theresulting synthesized image sequence back from the server 220. In thisinstance, the image capture device 104 serves as the client. In anotherembodiment, the image capture device 104 may transmit device informationto the server 220 in addition to the image of text indicating a desireto send the resulting synthesized image sequence to a designated client230. In one embodiment a client 230 or server 220 may transmit thesynthesized image sequence to a designated display 250. It should beappreciated that any combination of servers, image capture devices,clients, and displays may be implemented in the system 200 withoutdeparting from the scope of the present disclosure.

The clients 230, 240 are shown to include a display 232, 242, processingelectronics 234, 244, and a user input device 236, 246. The display 232,242 is configured to display the synthesized image sequences to theuser. The functionality of the display 232, 242 is described in greaterdetail with reference to the display 250 below.

The processing electronics 234, 244 may be configured to generate arequest to the server 220 for one or more synthesized image sequences.The processing electronics 234, 244 may be further configured to formatthe synthesized image sequences for display on the display 232, 242.According to one embodiment, the processing electronics 234, 244 may beconfigured to at least partially generate the synthesized imagesequences (e.g., to perform the task of the processing electronics 224of the server 220). For example, the processing electronics 234, 244 maybe configured to translate the image of the text into a machine readableformat. For another example, the processing electronics 234, 244 may beconfigured to generate the model information and send the modelinformation to the server 220 for rendering. For yet another example,the processing electronics 234, 244 may be configured to receive themodel information generated by the server 220 and to render the modelinformation into a synthesized image sequence. The processingelectronics 234, 244 may further be configured to otherwise manageinformation related to the synthesized image sequences.

The user input device 236, 246 is configured to receive a user inputrelating to the synthesized image sequences. For example, the user inputdevice 236, 246 may allow a user to request a synthesized imagesequence. As another example, the user input device 236, 246 may allow auser to provide a preference file or preference file data fortransmission to the server 220. As yet another example, the user inputdevice 236, 246 may allow a user to manipulate or recode the modelinformation.

The system 200 further includes a display 250. The display 250 is shownlocated remotely from a client device instead of part of a client device230, 240 as described above. The display 250 is shown as connected tothe network 210. According to various exemplary embodiments, the display250 may be part of the image capture device 104 (e.g., a display on amobile phone or other electronic device used to capture the image oftext) or may be connected to another device not otherwise part of thesystem 200 (e.g., a PC, laptop, another mobile phone, television screen,etc.). For example, the display 250 may be coupled to a camera of animage capture device. As another example, the display 250 may be coupledto a mobile phone. As yet another example, the display 250 is locatedremotely from the components of the system 200 over the network 210. Asyet another example, the display 250 may be any kind of touchscreendisplay.

The display 250 includes an input 252 and an output 254. The input 252receives a synthesized image sequence or other synthesized imagesequence from the server 220 (or client 230 and/or client 240) via thenetwork 210. The display 250 is configured to format the synthesizedimage sequence and present the synthesized image sequence on thedisplay. According to an exemplary embodiment, the server 220 may beconfigured to determine the type of output the display 250 can provideand to format the synthesized image sequence accordingly for display onthe display 250. For example, if the display 250 is capable ofdisplaying 3D images, the server 220 may be configured to generate 3Dimages or objects as at least part of the synthesized image sequence. Asanother example, if the display 250 is capable of high-definition (HD)display, the server 220 may be configured to generate an appropriatesynthesized image sequence for the display.

The output 254 of the display 250 may provide the server 220 and othercomponents connected to the network 210 with information relating to thedisplay of the synthesized image sequence. Such information may includedisplay information, screen resolution, digital content protection,communication format (e.g., digital or analog), etc. For example,display settings of the display 250 may be output to the server 220, andthe server 220 may use the display settings to configure the synthesizedimage sequence generated by the server 220. Other display informationmay include, for example, information relating to the success or failureof display of a particular synthesized image sequence, the number ofviews of the synthesized image sequence, and other usage information.

The system 200 may be configured to share information across the variouscomponents. For example, a created synthesized image sequence may beshared with multiple users (e.g., clients) instead of just the user thatrequested the synthesized image sequence. Such sharing may includesharing the actual synthesized image sequence, one or more filesincluding data that allows another system to recreate the synthesizedimage sequence (e.g., a preference file, model information, etc.), orany other information relating to the synthesized image sequence and itsgeneration.

Referring now to FIG. 3, a more detailed block diagram of processingelectronics 300 for completing the systems and methods of the presentdisclosure is shown, according to an exemplary embodiment. Theprocessing electronics 300 may be the processing electronics of server220 or clients 230, 240 of FIG. 2, according to an exemplary embodiment.The processing electronics 300 are generally configured to receive animage of text from an outside source (e.g., an image capture device).The processing electronics may further be configured to receivesupplemental information (e.g., one or more preference files, otherimages of text or pictures, auxiliary information, contextual cues,etc.). The processing electronics 300 are then configured to generate amodel information using at least some of the received information. Theprocessing electronics 300 are then further configured to generate asynthesized image sequence (e.g., video clip) using the modelinformation and, according to various embodiments, some of the receivedinformation and to provide the synthesized image sequence as an outputto a client.

The processing electronics 300 includes a processor 302 and memory 304.The processor 302 may be implemented as a general purpose processor, anapplication specific integrated circuit (ASIC), one or more fieldprogrammable gate arrays (FPGAs), a group of processing components, orother suitable electronic processing components. The memory 304 is oneor more devices (e.g., RAM, ROM, Flash memory, hard disk storage, etc.)for storing data and/or computer code for completing and/or facilitatingthe various processes described herein. The memory 304 may be or includenon-transient volatile memory or non-volatile memory. The memory 304 mayinclude data base components, object code components, script components,or any other type of information structure for supporting the variousactivities and information structures described herein. The memory 304may be communicably connected to the processor 302 and includes computercode or instructions for executing one or more processes describedherein (e.g., the processes shown in FIGS. 4-17).

The memory 304 includes a memory buffer 306. The memory buffer 306 isconfigured to receive data via a network (e.g., network 210) through aninput 355. The data may include image data (e.g., images of textreceived by the processing electronics 300), preference data (e.g., froma client or other electronic device), or other data. The data may bestored in the memory buffer 306 until the memory buffer 306 is accessedfor data by the various modules of the memory 304. For example, theimage to text module 314, context analysis module 316, and auxiliaryinformation module 318 may access the memory buffer 306 for images oftext received.

The memory 304 further includes configuration data 308. Theconfiguration data 308 includes data relating to the processingelectronics 300. For example, the configuration data 308 may includeinformation relating to a retrieval process of images (e.g., when themodule generation module 320 or image synthesis module 322 requests aseries of images or objects to create models or images, theconfiguration data 308 may be used to generate a request to transmit toan outside source for the images or objects, or the configuration data308 may be used to search for the images or objects in a database localto the processing electronics 300). As another example, theconfiguration data 308 may be used to configure communication betweenthe various modules of the processing electronics 300 (e.g., toconfigure the image to text module 314 to provide a text input tomodules 316, 318 for analyzing the text).

The memory 304 further includes a communication module 310. Thecommunication module 310 is configured to provide communicationcapability with other devices via the output 350. For example, thecommunication module 310 may be configured to take a finishedsynthesized image sequence generated by the processing electronics 300and to format the synthesized image sequence for transmission via theoutput 350. The communication module 310 may include logic forsupporting communications protocols (e.g., internet protocol, filetransfer protocol, etc.) or supporting server-client or peer-to-peernetwork relationships.

The memory 304 further includes a user interface module 312. The userinterface module 312 is configured to receive a user input from theinput 355 and to interpret the input for the other modules of theprocessing electronics 300. For example, the user interface module 312may receive a request for generation of a synthesized image sequence viathe input 355 and may be configured to use the request to providecommands to the various modules of the processing electronics 300 forgenerating the synthesized image sequence.

The memory 304 is shown to include various modules 314-326 for executingthe systems and methods described herein. The various modules 314-326are configured to receive images of text, preference data, and otherinformation from input 355 and/or a local database and formatted bymodules 306-312. The various modules 314-326 are then used to generatemodel information or one or more synthesized image sequences fortransmission to an outside source (e.g., a display).

The image to text module 314 is configured to receive an image of textfrom an outside source and to interpret the text in the image. The imageof text may be an image taken by a camera, scanner, or other electronicdevice and sent from the device to the processing electronics 300 eitherdirectly or via a network. The image to text module 314 is configured toidentify text in the image and to generate an interpretation of the textin a machine-readable format. The other modules of the processingelectronics 300 can then use the text in its machine-readable format tointerpret and analyze the text and to create synthesized image sequencesbased on the machine readable format of the text.

In one exemplary embodiment, the image to text module 314 may translatethe text in the image using optical character recognition (OCR). Inanother embodiment, the image to text module 314 may translate the textin the image using a handwriting recognition engine. The image to textmodule 314 may be configured to translate any type of text. For example,the text may be typeset (e.g., text created by a typewriter, printingpress, computer application, etc.) or the text may be handwritten, andthe image to text module 315 may distinguish the type of text and decidewhich translation technique to use. In one embodiment, the image to textmodule 314 may be configured to interpret shorthand or longhand notationin either typeset or handwritten form.

In one embodiment, the image to text module 314 may be implemented forany type of language (e.g., English, French, German, Chinese, Sanskrit,Morse code, Braille, hieroglyphs, etc.). The image to text module 314may further include a translator configured to translate the text in theimage from one language to another (e.g., if text in an image is French,then the French version of the text is translated into amachine-readable format, then translated again from French to anotherlanguage such as English). The image to text module 314 may receiveinstructions to translate between languages. For example, a preferencedata file may include an indication for a preferred language, tosubtitle the synthesized image sequence in the preferred language whileproviding dialogue in the native language, or to provide dialogue in thepreferred language (i.e., “dub” the dialogue).

The context analysis module 316 is configured to receive text in amachine-readable format from the image to text module 314. The contextanalysis module 316 is configured to interpret the received text. In oneembodiment, the interpretation of the text includes analyzing the textfor contextual cues. The contextual cues may relate to a setting, acharacter, a pose or action, and other defined objects as identified bythe text. For example, individual words may be identified by the contextanalysis module 316 that relate to a specific location or setting (e.g.,16^(th) Century England, Wisconsin, a house, Main Street, etc.), aspecific person (e.g., Shakespeare, Einstein, mother, father, etc.), aspecific action (e.g., running, talking, exercising, etc.), a pose(e.g., standing, sitting, etc.) or otherwise. As another example, aseries of words may be identified that relate to a specific action andspecific object (e.g., a person running, a car driving down a road, aphone ringing, etc.). In other words, the context analysis module 316 isconfigured to provide context to the literal interpretation of actionsdescribed by the text.

In addition to providing context to the literal interpretation of thetext, the context analysis module 316 may determine an origin of thetext. The origin of the text may be determined by identifying any slangor dialect in the words, by determining if the text has appeared in anybooks, plays, or other forms of media, or otherwise. For example, uponanalysis of the text, it may be determined that the origin of the textis from a Shakespearean play. A setting or location of 16^(th) CenturyEngland may then be determined by the context analysis module 316. Asanother example, if the text includes names of historical figures froman era and location, the era and location may be set as the setting orlocation (e.g., the name Julius Caesar may lead to an identification ofancient Rome as a setting or location, the name Abraham Lincoln mayindicate a setting of the United States Civil War Era, etc.).

The auxiliary information module 318 is configured to determineauxiliary information related to the image of text. Auxiliaryinformation may include a source of the text or image associated withthe text (e.g., a book or magazine which is the origin of the text).Auxiliary information may include, for a given image of text, anotherpassage in the source of text (e.g., if an image of one page of a bookis received, another page of the book may be used to determine auxiliaryinformation), information associated with the author of the source ofthe text, and other textual sources associated with the source of thetext (e.g., other texts in the same series as the source text, scholarlyanalyses of the source text, etc.).

The auxiliary information module 318 is configured to receive an inputof text from the image to text module 314 and/or an image or text froman outside source. The auxiliary information module 318 is configured touse the text and/or images to determine auxiliary information relatingto the text in an image. In one embodiment, the image to text module 314may be configured to distinguish text in an image that is not part of apassage or block of text. For example, an image of a whole page of amagazine may be captured. The image to text module 314 may interpret alltext on the page, which may include header and footer information (e.g.,the name of the magazine, title of an article, page numbers, etc.). Theauxiliary information module 318 is configured to receive all of theinterpreted text and to identify which of the text represents auxiliaryinformation and which of the text is part of the main text.

Further, the auxiliary information module 318 may receive an imageincluding text, symbols, and other identifiers, and is configured to usethe identifiers to determine auxiliary information. For example, theimages of text may include actual images in addition to the text. Theauxiliary information module 318 is configured to identify all non-textobjects in the image and to identify the non-text objects. For example,a picture of a basketball included with text in an image may beidentified, and the auxiliary information module 318 may conclude theblock of text is about basketball. According to one embodiment, theauxiliary information module 318 may receive an image of a character,setting, object, scenery, etc.

The auxiliary information module 318 may work in conjunction with thecontext analysis module 316 to determine context of the text. Forexample, a title of an article may be identified by the auxiliaryinformation module 318. The context analysis module 316 may then use thetitle of the article to look for contextual information relating to thearticle. For example, the context analysis module 316 may use the titleto determine a setting or location relating to the title (e.g., a titleincluding the words “Romeo” and “Juliet” may lead to the identificationof 16^(th) century Verona, Italy as a location). The auxiliaryinformation module 318 may then search for and receive map informationrelated to the determined setting (e.g., retrieve map data of Verona,Italy). As another example, the context analysis module 316 may look upa magazine that printed an article with the title determined by theauxiliary information module 318, and use information from the magazineto determine a setting or other property.

As another example, the auxiliary information module 318 may useinformation from the context analysis module 316. The context analysismodule 316 may provide determined contextual cues to the auxiliaryinformation module 318. The contextual cues are then used to determineauxiliary information. For example, the contextual cues may be used todetermine a source of the text (e.g., type of book, magazine, etc.).

In one embodiment, the auxiliary information module 318 may receive animage of a bar code (e.g., representing the ISBN number of a book,representing the ISSN number of a magazine, a UPC label, etc.). Theimage of the bar code may be sent along with an image of text (i.e., inthe same image or in a second image). The auxiliary information module318 may interpret the bar code to determine the origin of the image oftext, and provide the information to the context analysis module 316 todetermine contextual cues relating to the source. In another embodiment,an ISBN number of a book may be received by the auxiliary informationmodule 318 (the number being translated by the image to text module314), and the ISBN number may be used to determine the origin of theimage of text.

The model generation module 320 is configured to receive data from thecontext analysis module 316 (e.g., contextual cues), auxiliaryinformation module 318 relating to the context and other textinformation, and preference data 330. The model generation module 320may further receive contextual cues and auxiliary information from anoutside source via the input 355 instead of or in addition to themodules 316, 318. Using the data, the model generation module 320creates the model information (e.g., markup file, stream, etc.) from thedata. The model information general includes instructions (e.g.,computer code, mathematical representations, etc.) relating to theorientation, motion, and interaction of the objects (e.g., characters,props, etc.) and scenery. According to one embodiment, the modelinformation includes object location information (e.g., where an objectis in the scene, orientation of the object in relation to other objects,orientation of the object in relation to the scenery, etc.). Forexample, the object location information may include instructionsindicating that a character is near a window. According to anotherembodiment, the model information includes object activity information(e.g., what the object is doing). For example the object activityinformation may include instructions indicating that the character israising his arms and instructions that the window is opening. Accordingto other embodiments, the model generation module 320 may generateskeletons or wireframes of the setting, characters, and objects, and theimage synthesis module 322 may then render imagery over the skeletons.The wireframes of the settings may be generated based on map datareceived by the auxiliary information module 318.

According to one embodiment, the creation of the model informationincludes the process of determining types of images and objects to useto create the synthesized image sequence, receiving the images andobjects from a database or outside source, and creating the modelinformation using the images and objects. The model generation module320 may create the model information based on various types of settings.For example, the processing electronics 300 may receive instructions fora type or format in which the model information should be created. Themodel generation module 320 may generate model information havinginstructions for image sequences having two dimensional (2D) or threedimensional (3D) (or any combination thereof) objects and scenery. Asanother example, the processing electronics 300 may have a preset formatto use for generation if one is not provided. It should be understoodthat the type of model information generated by the model generationmodule 320 is not limited by the scope of the present disclosure.

In another embodiment, the model generation module 320 is configured tocreate a model information based on a series of images and objects. Asone example, a background image (e.g., scenery) may be set to displaythroughout the synthesized image sequence. Then, a series of objects maybe rendered for display (e.g., by the image synthesis module 322) in thesynthesized image sequence. The objects may include characters (e.g.,people identified and described by the text) or objects identified bythe text (e.g., car, ball, house, etc.). The model information maycontain instructions for moving the objects based on any number ofmotion algorithms. As a simple example, one object may move from left toright at a rate of 30 pixels per second. As a more complex example, themodel information may include information and instructions use togenerate a synthesized image sequence having multiple characters in ascene, each of which with a distinct “walking” speed as they move aroundin the scene. Further, each object may be animated. For example, eachobject may have multiple components, wherein each component may be movedor adjusted. As one example, a character can be made to look like he orshe is walking by moving the legs of the character.

One possible object to render in the synthesized image sequence may be acharacter (e.g., a person). The character may be a generic image of acharacter according to one embodiment. For example, the model generationmodule 320 may provide instructions for default or stock characters.According to another embodiment, the character may be based on adescription of the character in the text. The contextual cues determinedby module 316 may be used to edit the appearance of the character in themodel information. For example, if a character is said to have blondehair, the model information may provide instructions to render thecharacter with blonde hair. As another example, if a character is acelebrity character, an actual image of the celebrity may be used as thecharacter.

The character may be based on a user selection according to anotherembodiment. For example, a user preference from a preference file oranother input may be used to determine the appearance of the character.The preference file may indicate a preference for a specific person toappear, celebrity or otherwise (e.g., a family member of the user,friend of the user, the user, etc.). Alternatively, the preference filemay indicate a preference for only a type of person (e.g., people withblonde hair, only females, only males, only people of a particular raceof ethnicity, etc.) The same contextual cues and user preferenceinformation may be used to determine the appearance of other objects andthe background image or scenery in the synthesized image sequence aswell.

The image synthesis module 322 is configured to receive modelinformation from the model generation module 320. The image synthesismodule 322 may further receive data from context analysis module 316(e.g., contextual cues), auxiliary information module 318 relating tothe context and other text information, and preference data 330. Theimage synthesis module 322 may further receive contextual cues andauxiliary information from an outside source via the input 355 insteadof or in addition to the modules 316, 318. Using the data, the imagesynthesis module 322 generates (e.g., renders, creates, etc.) thesynthesized image sequence (e.g., video clip) from the model informationand other data, if any. The rendering process may use techniques orsoftware that are similar to those used for video game rendering.According to one embodiment, the creation of the image sequence includesthe process of determining types of images and objects to use to createthe sequence, receiving the images and objects from a database oroutside source, and creating the sequence using the images and objects.The image synthesis module 322 may create the synthesized image sequencebased on various types of settings. For example, the processingelectronics 300 may receive instructions for a type or format in whichthe synthesized image sequence should be created. As another example,the processing electronics 300 may have a preset format to use forgeneration if one is not provided. It should be understood that the typeof synthesized image sequence generated by the image synthesis module322 is not limited by the scope of the present disclosure.

In one embodiment, the image synthesis module 322 is configured tocreate a video based on a series of images (e.g., frames). The imagesynthesis module 322 may create a video with a frame rate of 24 framesper second (FPS), 30 FPS, 72 FPS, or any other speed. The video mayfurther include any number of transitional elements (e.g., fading in orout, panning across images, other slideshow effects, etc.). In anotherembodiment, the image synthesis module 322 may be configured to generatea three dimensional (3D) video. The 3D video may be configured fordisplay on an appropriate monitor.

The objects rendered in the scene of the synthesized image sequences maybe two dimensional (2D) or three dimensional (3D), according to anexemplary embodiment. As one example, after rendering a background 2Dimage in the scene, one or more 3D objects may be rendered in the scene.As another example, after rendering a background 3D image in the scene,one or more 3D objects may be rendered in the scene. The objects andimages rendered in the scene may be 2D, 3D, or a mixture of both. Theobjects rendered in the scene may then move on a 2D axis or 3D axis,according to an exemplary embodiment.

According to various embodiments, some steps and elements of the systemsand methods described in this disclosure may occur in the modelgeneration module 320, the image synthesis module 322, or anycombination thereof. For example, object information, characterinformation, scenery information, setting information, etc. may beincorporated into the synthesized image sequence by the model generationmodule 320 or the image synthesis module 322. According to oneembodiment, character information provided as contextual cues, auxiliaryinformation, or a user preference may be incorporated into the modelinformation as model generation module 320 generates the modelinformation. According to another embodiment, character information maybe incorporated into the synthesized image sequence as the imagesynthesis module 322 renders the synthesized image sequence. Accordingto one embodiment, the model generation module 320 may incorporate sizeor motion characteristics of the character into the model information,and the image synthesis module 322 may render appearance characteristicsinto the synthesized image sequence. According to another embodiment,the model generation module 320 may generate model information havingcharacter, scenery, or setting instructions based on the source text,contextual cues, and auxiliary information; however, the image synthesismodule 322 may render the synthesized image sequence based oninstructions in a preference data 330.

In one embodiment, the model generation module 320 and image synthesismodule 322 may be configured to include speech. For example, thesynthesized image sequence may include narration and a characterrendered in the synthesized image sequence may have dialogue, e.g., thecharacter may speak some of the text. The context analysis module 316may provide an indication to the model generation module 320 and imagesynthesis module 322 about which text may be dialogue. The dialogue ofthe synthesized image sequence may be in a different language than thetext. For example, image to text module 314 may translate a foreignlanguage text to the user's native language so that the dialogue isspoken in the user's native language. Other sounds (e.g., sound effects)may be included in the synthesized image sequence that fits the settingsand actions shown (e.g., gunfire in a synthesized image sequence that isrecreating a war scene).

In one embodiment, the images of text received by the processingelectronics 300 may be part of a comic book, graphic novel, slideshow,or other illustration that include non-text elements. The imagesynthesis module 322 may receive, in addition to contextual cues andauxiliary information, information relating to the other objects shownin the illustration and may be configured to use the other objects tocreate the synthesized image sequence. For example, objects shown in acomic book (e.g., a flying superhero, a laser beam, other specialeffects) may be animated by the image synthesis module 322. The objectsas shown in the comic book may be used to generate the objects in thesynthesized image sequence or the model generation module 320 or imagesynthesis module 322 may access a database and search for similarobjects to use. Map data received by the auxiliary information module318 may be used to generate the background or setting images.

The image synthesis module 322 may create, in order to supplement thesynthesized image sequence, various links and other interactive featuresin the synthesized image sequence. Such interactive features may be usedwith a display such as a touchscreen, or used when the display isconnected to an electronic device that includes a user input that cancontrol playback of the synthesized image sequence. For example, thesynthesized image sequence may include a link that, upon touching on atouchscreen or the pressing of a button on a user input, may take theviewer of the synthesized image sequence to a website related to thecontent of the synthesized image sequence.

The memory 304 is further shown to include preference data 330.Preference data 330 may either be stored in a database local to orremotely from the processing electronics 300, or may be received via apreference file transmitted to the processing electronics 300.Preference data 330 relates to model information preferences andsynthesized image sequence preferences for a user. Using the preferencedata, the model information generated by the model generation module 320may be modified or personalized from the default settings or from theauxiliary and contextual information. Similarly, using the preferencedata, the image synthesis module 322 may modify and personalize thesynthesized image sequence generated for a specific user. For example,the image synthesis module 322 may override instructions in the modelinformation, contextual cues, auxiliary information, etc. based on thepreference data. A preference file received by the processingelectronics 300 may be sent by a reader of the text or may be apreference file from another user or device (e.g., received over anetwork). For example, the processing electronics 300 may receive one ormore preference files from a friend or classmate of the user. Thepreference file may be generated from scratch based on user input or maybe generated based on a previously generated preference file. Thepreviously generated preference file may have been generated by the useror by another.

The preference data 330 may include character information. For example,if a user prefers a specific type of character (e.g., characters withblonde hair, famous people, only women, only men, family members, etc.),the preference data 330 may include such information. Using thepreference data 330, the image synthesis module 322 may replace acharacter who is to appear in a synthesized image sequence and replacehim with a character specified by the preference data 330. For example,in a video clip about a Shakespearean play, the preference data 330 maybe used to insert family members into the video clip instead of thetypical characters.

The preference data 330 may include scenery information (e.g.,background information). For example, if a user prefers a specific typeof scenery, the model generation module 320 or the image synthesismodule 322 may implement the preference. For example, in a video clip,if the preference data 330 indicates a preference to have a setting inNew York City, the video clip may be set in New York City regardless ofthe other content of the video clip.

The preference data 330 may include linguistic information. For example,if a user prefers a specific type of language or dialect in the videoclip, the model generation module 320 or the image synthesis module 322may implement the preference. This linguistic information may be used toreplace dialogue or other representations of text in the synthesizedimage sequence.

The preference data 330 may include time period information. Forexample, if a user prefers a synthesized image sequence “happens” in aspecific time period (e.g., ancient Rome, 16^(th) Century England,present day, etc.), the model generation module 320 or the imagesynthesis module 322 may implement the preference regardless of theother contextual cues and auxiliary information used to create thesynthesized image sequence.

The preference data 330 may include content rating information. Forexample, if a user prefers not to see any “mature” content regardless ofthe content of the text, the model generation module 320 may beconfigured not to model such content, and the image synthesis module 322may be configured to remove such content from the video clip. As anotherexample, if a user prefers the video clip to be viewed by children, themodel generation module 320 or the image synthesis module 322 may beconfigured to create a video clip that includes cartoon characters,animation, or other content that may be easier related to children.

The preference data 330 may include image format information. Forexample, the image format information may include instructions or logicspecifying or indicating the resolution or quality of the synthesizedimage sequence (e.g., high-definition, enhanced definition, standarddefinition, low-definition, 1080×1920 pixels, 720×1280 pixels, 480×704pixels, 480×640 pixels, etc.). The image format information may specifythe format of the image synthesis file (e.g., MPEG-2, MPEG-4, H.264,VP8, etc.). The image format information may include instructions orlogic specifying or indicating still images or video images;two-dimensional (2D) or three-dimensional (3D) images; cartoon animationor realistic rendering; color, sepia tone, monochromatic, or grayscaleimages.

The preference data 330 may be shared across a number of users,according to an exemplary embodiment. For example, the preference data330 may be stored in a preference file. The preference file may havebeen previously created or may be created by the processing electronics300. The preference file may then be shared with other users and otherprocessing electronics configured to receive requests from the otherusers.

The passage prediction module 324 is configured to schedule generationof synthesized image sequences based on reader behavior. The passageprediction module 324 receives a notification of receipt of an image oftext to be used for synthesized image sequence generation. The passageprediction module 324 may be used to determine when to create thesynthesized image sequence.

The passage prediction module 324 may be part of a prediction subsystemof the systems of the present disclosure. The prediction subsystem maybe able to predict or anticipate text that is about to be read by aperson. For example, the user may be reading a first textual passage inan article or book on an electronic device (e.g., an electronic book)and the electronic device may be configured to “jump ahead” anddetermine a second textual passage which text is about to be read. Inthis case, both the first and second textual passages are in anelectronic format. Upon determining such text, an image of the text maybe automatically taken by the electronic device and sent to theprocessing electronics 300, along with an indication of how soon thereader of the text will reach the text (e.g., reader information). Thepassage prediction module 324 may receive text source information fromthe auxiliary information module 318 and predict when a user will reacha subsequent (e.g., second) textual passage. For example, if a user(e.g., a student) takes a picture of printed text from the book “To Killa Mockingbird,” the auxiliary information module 318 may identify thesource of the text, and the passage prediction module 324 may predictwhen the reader will reach a subsequent passage in the book. In thiscase, the first textual passage is in a printed format, but thesynthesized image sequence is generated based on an electronic formatversion of the second passage. The passage prediction module 324 may beconfigured to receive the image of text and the reader information anduse the reader information to determine when to generate a synthesizedimage sequence for a particular image of text. The passage predictionmodule 324 predicts when a reader will reach a particular textualpassage and generates a synthesized image sequence for display for whenthe reader reaches the upcoming textual passage.

The passage prediction module 324 may further be configured to determinea textual passage that the reader is reading. According to oneembodiment, the passage prediction module 324 may determine that thereader is reading the textual passage that is being displayed. Accordingto another embodiment, the passage prediction module 324 may determinethat the reader is reading the textual passage of which the readercaptured an image. According to various other embodiments, the passageprediction module 324 may determine the textual passage that the readeris reading based on receiving a user input (e.g., bookmarking a page,turning a page, touching a portion of text on a touchscreen, providing apage number, providing a chapter and verse, etc.).

According to one embodiment, the prediction of when a reader will reacha particular textual passage may be based on knowledge of the amount oftext between the textual passage the reader is currently reading and thetextual passage provided to the passage prediction module 324. Theamount of text may be measured by the number of pages, paragraphs,sentences, words, or letters. According to another embodiment, thereading speed of the reader may be used to obtain the prediction. Thereading speed may be based on a number of pages per minute, an averagenumber of pages per minute, words per minute, density of the text, etc.The reading speed may be determined by the passage prediction module324, by the device capturing the images of text, or otherwise. In oneembodiment, the image capture device or another device may track usermovement (e.g., eye tracking) and use the movement information todetermine a reading speed or other property that may be used by thepassage prediction module 324. According to another embodiment, thepassage prediction module 324 may determine a textual passage beingcurrently read by the reader based on a previously determined textualpassage being read by the reader at that time, the reading speed of thereader, and an elapsed time.

The passage prediction module 324 may be configured to predict whichtextual passage will be read by a reader and will have imagerysynthesized based on an image synthesization speed of the processingelectronics 300 (e.g., the speed at which the processing electronics 300creates synthesized image sequences). For example, passage predictionmodule 324 may not select a passage for synthesization that is within agiven space (e.g., two pages, ten paragraphs, etc.) of the currentlyread text because the model generation module 320 and the imagesynthesis module 322 will not have time to generate a model and renderimagery.

The passage prediction module 324 may be configured to determine videoquality based on the reader information. For example, it may bedetermined that a reader is about to read a specific passage in twominutes based on a reading speed of the reader. The passage predictionmodule 324 may then project a quality of a synthesized image sequencethat can be generated in two minutes. Further, the passage predictionmodule 324 may always command the processing electronics 300 to generatea “simple” version of a synthesized image sequence for an image of textupon receipt of the text. The simple version of the synthesized imagesequence may be generated using a resource-limited schedule (e.g., on aschedule that minimizes time but still produces an acceptable quality).The simple version of the synthesized image sequence may then betransmitted to a display along with an indication that a better qualitysynthetic image sequence will be generated by the processing electronics300. The better quality synthesized image sequence is then sent to thedisplay when it is generated. That way, if a reader reaches a specificpassage, a simple version of a synthesized image sequence may always beavailable to the reader, and a better quality synthesized image sequencemay be available if there is enough time to generate the synthesizedimage sequence for the reader. The quality level of the synthesizedimage sequence may be changed based on a predicted time until the readerreaches the textual passage in question, according to an exemplaryembodiment. This process may also include logic for determining if thereis enough time to regenerate the synthesized image sequence at theimproved quality. The logic may use the reading speed or amount of textas described above.

The passage prediction module 324, when transmitting the synthesizedimage sequence to a display, may be configured to generate an indicia tothe display. The indicia may simply be used to alert the display thatthe synthesized image sequence is available for viewing in the future.The passage prediction module 324 may further transmit other indicia tothe display. For example, the passage prediction module 324 may alertthe display that a synthesized image sequence is currently being createdand may provide a predicted time until the synthesized image sequencewill be sent to the display, or a predicted time until the synthesizedimage sequence will be ready for viewing on the display. According tovarious embodiments, the indicia may be presented to the reader visually(e.g., shown on a screen), audibly (e.g., spoken word, alert sound,etc.), or tactilely (e.g., via vibration, haptic feedback, etc.).

In one embodiment, after the processing electronics 300 generates asynthesized image sequence for a first image of text, the passageprediction module 324 determines a second image of text or textualpassage about to be read by a reader and alerts the processingelectronics 300 to create a synthesized image sequence for the secondimage of text or textual passage.

The selection of the textual passage to be used for creation of asynthesized image sequence for future viewing by a reader may be done invarious ways. In one embodiment, the textual passage is chosen via thepotential interest to the reader of the textual passage. In otherembodiments, the textual passage may be based on a verb within thetextual passage, the number of action verbs within the textual passage,an adjective or number of action adjectives within the textual passage,a character within the textual passage, or another indicator, contextualcue, or linguistic characteristic of the textual passage.

The activities of the passage prediction module 324 are performed inparallel with a user reading the text in question, according to anexemplary embodiment. According to one embodiment, the passageprediction module 324 selects text such that processing electronics 300provide synthesized image sequences to the user in a substantiallyjust-in-time manner. According to another embodiment, the passageprediction module 324 may continue to read ahead to build up a libraryof synthesized image sequences. According to another embodiment, thepassage prediction module 324 may continue to read ahead to build up alibrary of model information, auxiliary information, and contextualcues, in which case only the image rendering need be done on ajust-in-time basis. According to another embodiment, the passageprediction module 324 may be configured to generate another synthesizedimage sequence associated with another predicted textual passage inresponse to completing generation of a synthesized image sequence.According to yet another embodiment, the passage prediction module 324may be used to trigger generation (or regeneration with improvedquality) of synthesized image sequences while a reader is taking a breakfrom reading. The activities of the prediction subsystem and passageprediction module 324 are described in greater detail in FIGS. 12-14.

The subscription module 326 is configured to handle subscriptioninformation related to the synthesized image sequence generation system.For example, the use of the system and processing electronics 300 may besubscription-based (e.g., a user may pay for the service of generatingthe synthesized image sequences, a user may pay for the service of usingthe generated model information, etc.). The subscription module 326 maybe configured to handle access to the system and processing electronics300 in such a setup. For example, the subscription module 326 may handlesubscriptions to the system (e.g., a user may sign up for and pay forthe services described herein, and the subscription module 326 may beused to manage the access to the services for the user). As anotherexample, the subscription module 326 may be configured to handlepurchases by a user of the system. As yet another example, thesubscription module 326 may be configured to handle output of the modelinformation or the synthesized image sequence (e.g., the subscriptionmodule 326 may have a list of users or devices that are subscribed tothe system and are configured to receive a specific series of videoclips or files used to generate the video clips upon generation of thevideo clips or files). As yet another example, the subscription module326 may allow a user to use the system on a per use basis (e.g., theuser has to pay for every use of the system or for every viewedsynthesized image sequence).

According to one exemplary embodiment, the subscription module 326 isconfigured to receive payment information in response to access to thesystem. The subscription module 326 may be configured to process thepayment by receiving the payment information (e.g., credit card number)and completing a transaction with a bank using the payment information.In one embodiment, the payment information is received by thesubscription module 326 after sharing the model information or thesynthesized image sequence. The payment information can be received foreach time the model information or synthesized image sequence isexported, or for each time the synthesized image sequence is accessed ona display located remotely from the processing electronics 300. The usermay be charged for each use of the synthesized image sequence orassociated file, or may be charged on a subscription basis (e.g.,charged for use of the model information or the synthesized imagesequence for a given time frame such as a day, week, month, etc.).

The advertisement module 328 is configured to handle advertisementsassociated with the generated video clips. For example, an advertisermay wish to use the system as a way to advertise a product. Theadvertisement module 328 may be configured to handle interaction betweenthe advertiser and the system and to edit the content of the video clipsbased on the advertiser preference.

In one embodiment, the advertiser may indicate a preference to display aspecific product in a video clip (e.g., the advertiser may have apreference file of its own). For example, there can be product placementin the video clip (e.g., a product such as a beverage may be insertedinto the video clip, a particular brand of beverage may be used whenevera beverage is in a video clip, a banner or other text may be insertedinto the video clip that promotes a product, etc.).

The advertisement module 328 may further be configured to handle otherpromotional aspects. For example, the advertisement module 328 mayreceive information from the other modules of the processing electronics300 relating to the synthesized image sequence. Such information mayinclude the original source of the text. The advertisement module 328may use such information to recommend other products to a viewer of thesynthesized image sequence. For example, if the synthesized imagesequence is generated from a source of text relating to a Shakespeareanplay, the advertisement module 328 may determine other Shakespeareanworks to recommend to the viewer or may recommend other adaptations,“remakes,” or influential sources of the text. For example, the Tamingof the Shrew may trigger a recommendation of Kiss Me Kate, or viceversa. The advertisement module 328 may recommend movies or televisionshows related to the synthesized image sequence if the image of textused to create the synthesized image sequence comes from a transcript ofanother movie or television program. The display of the recommendationsmay occur during the synthesized image sequence, before or after thesynthesized image sequence, or via another method (e.g., sending ane-mail or other message to a user independent of sending the synthesizedimage sequence to the user.

The processing electronics 300 further includes an output 350 and input355. The output 350 is configured to provide an output to a client,display, or other electronic device as described above. Outputs mayinclude a generated synthesized image sequence, synthesized imagesequence information, preference files, etc. The input 355 is configuredto receive images of text, preference file information, and otherinformation relating to the generation of synthesized image sequence asdescribed above.

According to an exemplary embodiment, the processing electronics 300 mayreceive a previously synthesized data file. According to one embodiment,the previously synthesized data file includes a synthesized imagesequence and data relating to the synthesized image sequence eithergenerated by the processing electronics 300 or an outside source.According to another embodiment, the previously synthesized data fileincludes model information. The processing electronics 300 may use thepreviously synthesized data file to create a new synthesized imagesequence. According to one embodiment, the previously synthesized datafile may be a data file created for or by a different user.

According to an exemplary embodiment, the processing electronics 300exports a file that allows another system for another user to generate asynthesized image sequence using the same information the processingelectronics 300 used to generate its synthesized image sequence. Thefile may include the model information, images and objects used togenerate the synthesized image sequence, contextual cues, preferencefiles or preference data, auxiliary information, etc. In one embodiment,the file simply includes the actual synthesized image sequence. Inanother embodiment, the file includes only preference information suchas character information, scenery information, linguistic information,time period information, content rating information, and otherinformation that can be used to create the synthesized image sequence.

The processing electronics 300 may be configured to create the file. Inone embodiment, the model generation module 320 or the image synthesismodule 322 may be configured to create the file using all of the datareceived from other modules. The file may include information from auser input, according to an exemplary embodiment. The user input mayrelate to various preferences and may include character information,scenery information, linguistic information, time period information,content rating information, etc.

The processing electronics 300 may be configured to receive an exportedfile as described above and to use the file to generate a synthesizedimage sequence. The processing electronics 300, upon receipt of theexported file, may be configured to provide an indication via a displayto a device associated with the processing electronics 300 that the filewas received. The user of the device may then have the option to requesta generation of the synthesized image sequence.

In an exemplary embodiment, the created and exported file may be storedin a database either local to or remotely located from the processingelectronics 300. Other devices may then access the database to accessthe file instead of receiving the file directly from the processingelectronics 300, according to an exemplary embodiment. The database maybe configured to be searchable based on the content of the file (e.g.,searchable by the type of synthesized image sequence format, modelinformation, auxiliary information, contextual cue information,preference information, etc.), the user who generated or uploaded thefile, etc. Using the database and the various electronic devices thatmay connect to the database, a sharing network may be configured thatallows preference files and other files relating to synthesized imagesequences to be shared between users. The sharing network may be of anysuitable structure, for example, client-server, peer-to-peer, etc.

Referring generally to FIGS. 4-17, various processes are shown anddescribed that may be implemented using the systems and methodsdescribed herein. The processes of FIGS. 4-17 may be implemented usingthe system 200 of FIG. 2 and the processing electronics 300 of FIG. 3.

Referring now to FIG. 4, a flow diagram of a process 400 for convertinga user-selected printed text to a synthesized image sequence is shown,according to an exemplary embodiment. The process 400 includes capturinga first image of a printed text (step 402). The image may be captured byan image capture device 104 as described above. The process 400 furtherincludes generating a model information associated with the text (step404). The generation of the model information may be done by theprocessing electronics 300 as described above.

Referring now to FIG. 5, another flow diagram of a process 500 forconverting a user-selected printed text to a synthesized image sequenceis shown, according to an exemplary embodiment. The process 500 includescapturing a first image of a printed text (step 502). The process 500further includes translating the text of the first image into amachine-readable format (step 504). The translation is made using theimage to text module 314, according to an exemplary embodiment. Theprocess 500 further includes analyzing the text for a contextual cue(step 506). The analysis may be performed by the context analysis module316, according to an exemplary embodiment. The process 500 furtherincludes generating a model information associated with the text basedon the text and on the contextual cue (step 508).

Referring now to FIG. 6, another flow diagram of a process 600 forconverting a user-selected printed text to a synthesized image sequenceis shown, according to an exemplary embodiment. The process 600 includescapturing a first image of a printed text (step 602). The process 600further includes receiving auxiliary information (step 610) andgenerating a model information associated with the text based on thetext and on the auxiliary information (step 612). The process 600 mayalso include capturing a second image (step 604) and analyzing thesecond image for auxiliary information (step 606). The process 600 mayalso include analyzing another passage in the source of the text forauxiliary information (step 608). Steps 606 and 608 may be performed bythe auxiliary information module 318, according to an exemplaryembodiment.

Referring now to FIG. 7, a flow diagram of a process 700 for convertinga user-selected printed text to a synthesized image sequence is shown,according to an exemplary embodiment. The process 700 includes capturinga first image of printed text (step 706), generating a model informationassociated with the text based on the text and on the preference datafiles (step 708), generating a synthesized image sequence based on themodel information (step 710), and causing the synthesized image sequenceto be displayed on a designated display (step 712). The data in thepreference data files is similar to the data of preference data 330,according to an exemplary embodiment. The process 700 may also includereceiving a preference data file from another user (step 702) andgenerating a preference data file in response to user input (step 704).The process 700 may include both steps 702 and 704 or only one of steps702 and 704, according to various embodiments.

Referring now to FIG. 18, a flow diagram of a process 1800 forconverting a user-selected printed text to a synthesized image sequenceis shown, according to an exemplary embodiment. The process 1800includes capturing a first image of printed text (step 1806), generatinga model information associated with the text (step 1808), generating asynthesized image sequence based on the model information based on themodel information and on the preference data files (step 1810), andcausing the synthesized image sequence to be displayed on a designateddisplay (step 1812). The data in the preference data files is similar tothe data of preference data 330, according to an exemplary embodiment.The process 1800 may also include receiving a preference data file fromanother user (step 1802) and generating a preference data file inresponse to user input (step 1804). The process 1800 may include eitherboth of the steps 1802 and 1804 or only one of steps 1802 and 1804,according to various embodiments.

Referring to FIG. 8, a flow diagram of a process 800 for converting auser-selected printed text to a synthesized image sequence is shown,according to an exemplary embodiment. The process 800 includes receivingan image of text (step 802) and in response to receiving the image,generating a model information associated with the text (step 804).

Referring to FIG. 9, a flow diagram of a process 900 for converting auser-selected printed text to a synthesized image sequence usingcontextual cues, auxiliary information, and preference files is shown,according to an exemplary embodiment. The process 900 includes receivinga preference data file (step 902) and an image of text (step 904). Theprocess 900 further includes translating the text of the first imageinto a machine readable format (step 906). The process 900 furtherincludes analyzing the text for a contextual cue (step 908) using, forexample, the context analysis module 316. The process 900 furtherincludes determining the source of the text (step 914), analyzinganother passage in the source of the text for auxiliary information(step 916), and receiving auxiliary information (step 918). The process900 may include any combination of steps 914, 916, 918 for receivingauxiliary information. The process 900 further includes generating amodel information associated with the text based on the text, preferencedata file, contextual cue, and auxiliary information (step 920) andgenerating a synthesized image sequence based on the model information(step 922). The process 900 may also include receiving a second image(step 910) and analyzing the second image for auxiliary information(step 912) using, for example, the auxiliary information module 318.

Referring to FIG. 10, a flow diagram of a computerized process 1000 forsharing a synthesized image sequence generated from a user-selected textis shown, according to an exemplary embodiment. The process 1000includes generating a model information associated with a textualpassage (step 1002). The process 1000 further includes exporting a file(step 1004). The file is configured to enable another user to generatethe synthesized image sequence associated with the textual passage.

Referring to FIG. 11, a flow diagram of a process 1100 for sharing asynthesized image sequence generated from a user-selected text is shown,according to an exemplary embodiment. The process 1100 may be executedby the processing electronics 300 of FIG. 3 and more particularly thesubscription module 326. The process 1100 includes generating a filebased on at least one user input (step 1106). The file may be apreference file relating to user preferences, according to an exemplaryembodiment. The process 1100 further includes generating a modelinformation associated with a textual passage (step 1108). The process1110 further includes exporting a file from a first computer, the fileconfigured to enable another user to generate the synthesized imagesequence associated with the text (step 1110). The file may be a modelinformation, a preference data file, an image or object file, asynthesized image sequence, etc. The process 1100 further includescausing an indication to be displayed on a second computer in relationto a copy of the textual passage, the indication indicating availabilityof a file to generate a synthesized image sequence for the textualpassage (step 1112). The process 1100 further includes causing a user ofthe second computer to be charged for using the file (step 1114) andreceiving a payment in response to sharing the file (step 1116). Steps1114 and 1116 may be managed and executed by, for example, thesubscription module 326. The process 1100 may also include receiving auser selection of a textual passage (step 1102) and capturing an imageof the textual passage from a printed format (step 1104).

Referring generally to FIGS. 12-14, processes for executing the methodsdescribed with reference to the passage prediction module 324 of FIG. 3are shown. Referring now to FIG. 12, a flow diagram of a computerizedprocess 1200 for converting a textual passage to a synthesized imagesequence is shown, according to an exemplary embodiment. The process1200 includes determining a first textual passage currently being readby a user (step 1202), predicting a second textual passage that will beread by a user (step 1204). The prediction may be made by the passageprediction module 324, according to an exemplary embodiment. The process1200 further includes generating a synthesized image sequence associatedwith the second textual passage (step 1206).

Referring now to FIG. 13, a flow diagram of a process 1300 forconverting a textual passage to a synthesized image sequence is shown,according to an exemplary embodiment. The process 1300 includesdetermining a reading speed of the user (step 1302). The reading speedmay be based on a number of pages per minute, an average number of pagesper minute, words per minute, etc. The process 1300 further includesdetermining a first textual passage currently being read by a user (step1304), predicting a second textual passage that will be read by a userbased on the reading speed of the user and the amount of text betweenthe first textual passage and the second textual passage (step 1306).The first textual passage may be automatically determined as the textpresently displayed to the user if the text is displayed on anelectronic display, according to an exemplary embodiment. The process1300 further includes selecting the second textual passage based oninterest to the user as a synthesized image sequence (step 1308). Thestep 1308 includes selecting the second textual passage based onpotential interest to the reader of the textual passage, verbs,adjectives, or a combination of verbs and adjectives within the textualpassage (describing the action in the textual passage), or otherwise.The process 1300 further includes generating a synthesized imagesequence associated with the second textual passage at a first quality(step 1310). The process 1300 further includes generating an indicia inrelation to the second textual passage (step 1310). The process 1300 maythen return to step 1304, determining another (e.g., a third) textualpassage that is currently being read by the user. For example, while theprocess is predicting the second textual passage and generating theassociated synthesized image sequence, the user has likely continuedreading such that when the process returns to step 1304, the textualpassage that the user is currently reading is likely a another (e.g. athird) textual passage. A fourth textual passage will be predicted basedon the third textual passage, and so on.

Referring now to FIG. 14, a flow diagram of a process 1400 forconverting a textual passage to a synthesized image sequence is shown,according to an exemplary embodiment. The process 1400 includesdetermining a first textual passage currently being read by a user (step1401), predicting a second textual passage that will be read by a user(step 1402), and generating a synthesized image sequence associated withthe second textual passage at a first quality (step 1404). The process1400 further includes determining if the predicted time until the userreads the second textual passage is greater than a predicted time toregenerate the synthesized image sequence at a second quality betterthan the first quality (step 1406). In other words, step 1406 includesdetermining if there is enough time to create a better synthesized imagesequence. If there is enough time, the process 1400 includesregenerating the synthesized image sequence at the second quality (step1408). If there is not enough time, the process 1400 skips step 1408.The process 1400 then includes predicting a third textual passage thatwill be read by the user (step 1410). Predicting the third textualpassage may be based on determining the passage currently being read bythe user, the reading speed of the user, and the amount of text betweenthe textual passage currently being read and the third textual passage.The process 1400 further includes generating a second synthesized imagesequence associated with the third textual passage (step 1412).According to one embodiment, the first textual passage and the secondtextual passage are from the same source text. According to anotherembodiment, the third textual passage is from the same source text asthe first textual passage and the second textual passage.

According to an exemplary embodiment, the systems and methods of thepresent disclosure may be used to convert a textual passage into asynthesized image sequence in “real-time” (e.g., as the text is beingwritten or typed). According to one embodiment, text may continually besent to processing electronics 300 for creating a model information asthe text is being created. According to another embodiment, text maycontinually be sent to processing electronics 300 for creating asynthesized image sequence as the text is being created. According toother embodiments, as the textual passage is being written, an image ofthe text may continually taken and sent to the processing electronics300 for creating a model information and a synthesized image sequence.By generating synthesized image sequences in real-time, it allows forstreaming of the synthesized image sequence since portions of thesynthesized image sequences are not generated or loaded on a displayuntil one portion of the synthesized image sequences have already beenviewed.

In one embodiment, when a user finishes typing or writing a sentence orparagraph, the text may be automatically sent to the processingelectronics 300. In another embodiment, an indication that a writer hasfinished writing a sentence or paragraph may trigger the text to be sentto the processing electronics 300. The indication may be a manualindication from a writer, may be automatically triggered upon thepressing of an “Enter” or “Return” button on a keyboard, or otherwise.In yet another embodiment, the text is received by the processingelectronics as the text is being typed.

In one embodiment, when a user finishes typing or writing a sentence orparagraph, the image of the text may be automatically taken by an imagecapture device. In another embodiment, an indication that a writer hasfinished writing a sentence or paragraph may be sent to the imagecapture device in order to trigger the capture of the image of text. Theindication may be a manual indication from a writer, may beautomatically triggered upon the pressing of an “Enter” or “Return”button on a keyboard, or otherwise.

In addition to the contextual cues as described above, when analyzingthe images of text in “real-time,” the processing electronics 300 andmore particularly the context analysis module 316 may determine a stagedirection as a contextual cue. In one embodiment, the “real-time” aspectmay be applied when a writer is writing a script for a play or otherlive performance. The writer may include stage directions (e.g., exitstage left, enter stage right, etc.) with the other text. When the textis eventually received by the processing electronics 300, the processingelectronics 300 may recognize the stage directions and use the stagedirections to control the motions of various characters and objects inthe model information and synthesized image sequence. Deleting a passageof text may return the displayed image to a previous stage. For example,the writer may delete a stage direction “Character A exits stage left,”and in response, the processing electronics 300 may cause Character A tobe returned to the displayed stage. The writer then may type “CharacterA exits stage right,” and when the text is received by the processingelectronics 300, the processing electronics 300 generate a modelinformation and synthesized image sequence of Character A exiting stageright.

Referring generally to FIGS. 15-17, various methods for converting atextual passage to synthesized image sequences in “real-time” are shownin greater detail. Referring now to FIG. 15, a flow diagram of a process1500 for generating a synthesized image sequence in real-time is shown,according to an exemplary embodiment. The process 1500 includesgenerating a model information associated with a textual passage inresponse to the textual passage being written (step 1502). As discussedabove, the textual passage being written may be “instantly” analyzed(e.g., analyzed shortly after creation of the text).

Referring now to FIG. 16, another flow diagram of a process 1600 forgenerating a synthesized image sequence in real-time is shown, accordingto an exemplary embodiment. The process 1600 includes receiving a signalindicating the end of a paragraph (step 1602). The signal may be thepressing of the “Enter” or “Return” button on a keyboard or may bemanually indicated by a user. The process 1600 further includesanalyzing the text for a contextual cue (step 1604) using, for example,the context analysis module 316. The process 1600 further includesanalyzing another passage in the source of the text for auxiliaryinformation (step 1606) using, for example, the auxiliary informationmodule 318. The process 1600 further includes receiving the auxiliaryinformation (step 1608). The process 1600 further includes generating amodel information associated with the textual passage in response to thetextual passage being written based on text, the contextual cue, theauxiliary information, and a preference data file (step 1610). Theprocess 1600 further includes generating a synthesized image sequencebased on the model information (step 1612).

Referring now to FIG. 17, another flow diagram of a process 1700 forgenerating a synthesized image sequence in real-time is shown, accordingto an exemplary embodiment. The process 1700 includes receiving an imageof text (step 1702). The text may be part of a textual passage beingwritten in real-time. The process 1700 further includes translating thetext of the image into a machine-readable format (step 1704). Theprocess 1700 further includes generating a model information associatedwith a textual passage in response to the textual passage being written(step 1706).

The systems and methods of the present disclosure describe visualcontent associated with synthesized image sequences. However, it shouldbe understood that the synthesized image sequences may be enhanced withother outputs. For example, a video clip generated by the systems andmethods described herein may include sound. The synthesized imagesequences as described in the present disclosure are not limiting; i.e.,the synthesized image sequences may include other components.

The construction and arrangement of the elements of the systems andmethods as shown in the exemplary embodiments are illustrative only.Although only a few embodiments of the present disclosure have beendescribed in detail, those skilled in the art who review this disclosurewill readily appreciate that many modifications are possible (e.g.,variations in sizes, dimensions, structures, shapes and proportions ofthe various elements, values of parameters, mounting arrangements, useof materials, colors, orientations, etc.) without materially departingfrom the novel teachings and advantages of the subject matter recited.For example, elements shown as integrally formed may be constructed ofmultiple parts or elements. The elements and assemblies may beconstructed from any of a wide variety of materials that providesufficient strength or durability, in any of a wide variety of colors,textures, and combinations. Additionally, in the subject description,the word “exemplary” is used to mean serving as an example, instance orillustration. Any embodiment or design described herein as “exemplary”is not necessarily to be construed as preferred or advantageous overother embodiments or designs. Rather, use of the word exemplary isintended to present concepts in a concrete manner. Accordingly, all suchmodifications are intended to be included within the scope of thepresent disclosure. The order or sequence of any process or method stepsmay be varied or re-sequenced according to alternative embodiments. Anymeans-plus-function clause is intended to cover the structures describedherein as performing the recited function and not only structuralequivalents but also equivalent structures. Other substitutions,modifications, changes, and omissions may be made in the design,operating conditions, and arrangement of the preferred and otherexemplary embodiments without departing from scope of the presentdisclosure or from the scope of the appended claims.

The present disclosure contemplates methods, systems and programproducts on any machine-readable media for accomplishing variousoperations. The embodiments of the present disclosure may be implementedusing existing computer processors, or by a special purpose computerprocessor for an appropriate system, incorporated for this or anotherpurpose, or by a hardwired system. Embodiments within the scope of thepresent disclosure include program products comprising machine-readablemedia for carrying or having machine-executable instructions or datastructures stored thereon. Such machine-readable media can be anyavailable media that can be accessed by a general purpose or specialpurpose computer or other machine with a processor. By way of example,such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROMor other optical disk storage, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to carry or storedesired program code in the form of machine-executable instructions ordata structures and which can be accessed by a general purpose orspecial purpose computer or other machine with a processor. Wheninformation is transferred or provided over a network or anothercommunications connection (either hardwired, wireless, or a combinationof hardwired or wireless) to a machine, the machine properly views theconnection as a machine-readable medium. Thus, any such connection isproperly termed a machine-readable medium. Combinations of the above arealso included within the scope of machine-readable media.Machine-executable instructions include, for example, instructions anddata which cause a general purpose computer, special purpose computer,or special purpose processing machines to perform a certain function orgroup of functions.

Although the figures show a specific order of method steps, the order ofthe steps may differ from what is depicted. Also two or more steps maybe performed concurrently or with partial concurrence. Such variationwill depend on the software and hardware systems chosen and on designerchoice. All such variations are within the scope of the disclosure.Likewise, software implementations could be accomplished with standardprogramming techniques with rule based logic and other logic toaccomplish the various connection steps, processing steps, comparisonsteps and decision steps.

What is claimed is:
 1. A method of converting user-selected printed textto a synthesized image sequence, comprising: capturing a first image ofprinted text; and determining the source of the text contained in thefirst image.
 2. The method of claim 1, further comprising generating asynthesized image sequence based on the printed text.
 3. The method ofclaim 1, further comprising capturing the first image by camera.
 4. Themethod of claim 3, wherein the camera is coupled to a personalelectronic device.
 5. The method of claim 1, further comprisingtranslating the text of the first image into a machine readable format.6. The method of claim 5, further comprising generating modelinformation based on the text translated into the machine readableformat.
 7. The method of claim 1, further comprising generating modelinformation based on auxiliary information.
 8. The method of claim 7,wherein the auxiliary information comprises information associated withthe source of the text.
 9. The method of claim 8, wherein the auxiliaryinformation comprises information associated with the author of thesource of the text.
 10. The method of claim 1, further comprisinggenerating model information based on a preference data file.
 11. Themethod of claim 10, further comprising receiving a preference data filefrom another user.
 12. The method of claim 10, further comprisinggenerating a preference data file based on a previously generatedpreference data file.
 13. A computerized method of sharing a synthesizedimage sequence generated from user-selected text, comprising: capturingan image; acquiring a textual passage from a machine readable textcontained in the image; and exporting a file, the file configured toenable another user to generate a synthesized image sequence associatedwith the textual passage.
 14. The method of claim 13, wherein capturingthe image includes capturing the image by camera.
 15. The method ofclaim 14, wherein the camera is coupled to a personal electronic device.16. The method of claim 14, wherein the camera is coupled to a mobilephone.
 17. The method of claim 13, wherein the file comprises modelinformation.
 18. The method of claim 13, wherein the file comprises thesynthesized image sequence.
 19. The method of claim 13, furthercomprising generating the synthesized image sequence based on the file.20. The method of claim 13, wherein the file is exported from a firstcomputer; and further comprising causing an indication to be displayedon a second computer in relation to a copy of the textual passage, theindication indicating availability of a file to generate a synthesizedimage sequence for that passage.
 21. A computerized method of convertinga textual passage to a synthesized image sequence, comprising:determining a first textual passage currently being read by a user;predicting a second textual passage that will be read by the user; andgenerating an indicia in relation to the second textual passage.
 22. Themethod of claim 21, further comprising generating a synthesized imagesequence associated with the second textual passage.
 23. The method ofclaim 21, wherein the indicia indicates that a synthesized imagesequence has been generated for the second textual passage.
 24. Themethod of claim 21, wherein the indicia indicates a predicted time tocomplete generation of a synthesized image sequence for the secondtextual passage.
 25. The method of claim 21, wherein the indiciaindicates a predicted time until a synthesized image sequence for thesecond textual passage can begin playing.
 26. The method of claim 21,further comprising predicting the second textual passage that will beread by the user based on the amount of text between the first textualpassage and the second textual passage.
 27. The method of claim 21,further comprising predicting the second textual passage that will beread by the user based on the reading speed of the user.
 28. The methodof claim 27, further comprising predicting the second textual passagethat will be read by the user based on an image synthesization speed ofa processing electronics.
 29. The method of claim 21, further comprisinggenerating a synthesized image sequence at a quality level based on apredicted time until the user reads the second textual passage.
 30. Themethod of claim 21, further comprising predicting a third textualpassage that will be read by the user.